Brain-inspired Neocortical Computing Algorithm and Architecture Design for Intelligent Visual Recognition Applications
Date Issued
2012
Date
2012
Author(s)
Tsai, Chuan-Yung
Abstract
Thanks to the ceaseless driving force of the Moore''s law, intelligent visual data analytics which could be done only with gigantic mainframe computers has now started to penetrate into our daily lives. As we are moving toward the future visual recognition applications, in which a lot more possibilities (e.g. intelligent surveillance, driver-less cars, etc.) can emerge, developing a widely-applicable, low-power and real-time intelligent visual recognition hardware is an inevitable research trend. And among all research goals, attaining human brain-like performances is undoubtedly the ultimate one. In this dissertation, we will first review the basic visual neuroscience and the fundamental design concepts and theories behind the rising brain-mimicking recognition algorithms, which we called the Neocortical Computing (NC) model. In Chapter 2, we will introduce the basic NC algorithm -- HMAX as our starting framework, which has demonstrated promising performances on image recognition and basic video recognition applications. Then in Chapter 3, we will discuss the deficiencies of the basic HMAX in future applications, where we will have to extend it to more difficult and closer-to-real-life recognition tasks like 1) action/activity video recognition and 2) large-scale image/video recognition and learning. To address the first issue, we proposed an advanced NC algorithm that combines the HMAX with a brain-inspired Reservoir Kernel, which can function as a dimension-lifting kernel with temporal memory that integrates the shorter temporal information (atomic actions) extracted by the HMAX network. Experimental results show over 1.4x recognition accuracy increase when running on the latest human action/activity dataset. To address the second issue, we proposed a brain-inspired Feature-Selective Hashing scheme for indexing/searching the object instances efficiently. Experimental results show that it can reduce at most 90% of recognition time with less than 1% accuracy drop, and it also provides computation scalability when the number of learned object instances increases. In Chapter 4 and 5, we will introduce the proposed NC processor''s architecture, including the grey matter-like homogeneous 36-core architecture with event-driven hybrid MIMD execution and white matter-like Kautz NoC architecture with fault/congestion avoidance and redundancy-free multicast. Based on these design features, the proposed architecture successfully solves the design challenges including 1) scalability requirement, 2) GOPS-level computation complexity and 3) Tb/s-level communication bandwidth requirement, and can efficiently accelerate the brain-mimicking NC algorithms; thus the goal of widely-applicable power-efficient real-time visual recognition is also reached. It is implemented using TSMC 65nm technology on a 4.5x4.5mm2 die with 360GOPS peak performance, 2.3Tb/s aggregated NoC bandwidth and 205mW average power consumption when running at 250MHz and 1.0V. It achieves 1.0TOPS/W overall power efficiency and 151Tb/s/W NoC power efficiency, which are both higher than state-of-the-art visual recognition processors. NC applications, including object/face/scene image recognition (128x128 or 256x256) and action/sport video recognition (128x128) can be executed at speed up to 130fps. To sum up, this dissertation presents our exploration and realization of the brain-inspired Neocortical Computing algorithm and architecture, which can serve a wide range of intelligent visual recognition applications.
Subjects
Neocortical Computing
Intelligent Visual Recognition
IC Design
Type
thesis
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